TITLE: ANALYSIS OF THE UNEMPLOYMENT RATE IN INDIA¶

The dataset is set to analyze the rate of unemployment 
in India in specific locations and compare them to one another

OBJECTIVES;
1. Clean and thouroughly analyze the dataset 
2. Analyze some important trends in the dataset.
3. Answer data driven questions and showcase answers using charts.
In [1]:
#import libraries and datasets
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go

data=pd.read_csv(r"C:\Users\AISHAT\Desktop\PROJECTS\Cognorise\archive\Unemployment in India.csv")
data2=pd.read_csv(r"C:\Users\AISHAT\Desktop\PROJECTS\Cognorise\archive\Unemployment_Rate_upto_11_2020.csv")
data
Out[1]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Area
0 Andhra Pradesh 31-05-2019 Monthly 3.65 11999139.0 43.24 Rural
1 Andhra Pradesh 30-06-2019 Monthly 3.05 11755881.0 42.05 Rural
2 Andhra Pradesh 31-07-2019 Monthly 3.75 12086707.0 43.50 Rural
3 Andhra Pradesh 31-08-2019 Monthly 3.32 12285693.0 43.97 Rural
4 Andhra Pradesh 30-09-2019 Monthly 5.17 12256762.0 44.68 Rural
... ... ... ... ... ... ... ...
763 NaN NaN NaN NaN NaN NaN NaN
764 NaN NaN NaN NaN NaN NaN NaN
765 NaN NaN NaN NaN NaN NaN NaN
766 NaN NaN NaN NaN NaN NaN NaN
767 NaN NaN NaN NaN NaN NaN NaN

768 rows × 7 columns

DATA CLEANING¶

In [2]:
#column names
data.columns
Out[2]:
Index(['Region', ' Date', ' Frequency', ' Estimated Unemployment Rate (%)',
       ' Estimated Employed', ' Estimated Labour Participation Rate (%)',
       'Area'],
      dtype='object')
In [3]:
#remove spaces in column names
data.columns=data.columns.str.strip()
data.columns
Out[3]:
Index(['Region', 'Date', 'Frequency', 'Estimated Unemployment Rate (%)',
       'Estimated Employed', 'Estimated Labour Participation Rate (%)',
       'Area'],
      dtype='object')
In [4]:
data
Out[4]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Area
0 Andhra Pradesh 31-05-2019 Monthly 3.65 11999139.0 43.24 Rural
1 Andhra Pradesh 30-06-2019 Monthly 3.05 11755881.0 42.05 Rural
2 Andhra Pradesh 31-07-2019 Monthly 3.75 12086707.0 43.50 Rural
3 Andhra Pradesh 31-08-2019 Monthly 3.32 12285693.0 43.97 Rural
4 Andhra Pradesh 30-09-2019 Monthly 5.17 12256762.0 44.68 Rural
... ... ... ... ... ... ... ...
763 NaN NaN NaN NaN NaN NaN NaN
764 NaN NaN NaN NaN NaN NaN NaN
765 NaN NaN NaN NaN NaN NaN NaN
766 NaN NaN NaN NaN NaN NaN NaN
767 NaN NaN NaN NaN NaN NaN NaN

768 rows × 7 columns

In [ ]:
In [5]:
#data2
data2.head(10)
Out[5]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Region.1 longitude latitude
0 Andhra Pradesh 31-01-2020 M 5.48 16635535 41.02 South 15.9129 79.74
1 Andhra Pradesh 29-02-2020 M 5.83 16545652 40.90 South 15.9129 79.74
2 Andhra Pradesh 31-03-2020 M 5.79 15881197 39.18 South 15.9129 79.74
3 Andhra Pradesh 30-04-2020 M 20.51 11336911 33.10 South 15.9129 79.74
4 Andhra Pradesh 31-05-2020 M 17.43 12988845 36.46 South 15.9129 79.74
5 Andhra Pradesh 30-06-2020 M 3.31 19805400 47.41 South 15.9129 79.74
6 Andhra Pradesh 31-07-2020 M 8.34 15431615 38.91 South 15.9129 79.74
7 Andhra Pradesh 31-08-2020 M 6.96 15251776 37.83 South 15.9129 79.74
8 Andhra Pradesh 30-09-2020 M 6.40 15220312 37.47 South 15.9129 79.74
9 Andhra Pradesh 31-10-2020 M 6.59 15157557 37.34 South 15.9129 79.74
In [6]:
data2.columns
Out[6]:
Index(['Region', ' Date', ' Frequency', ' Estimated Unemployment Rate (%)',
       ' Estimated Employed', ' Estimated Labour Participation Rate (%)',
       'Region.1', 'longitude', 'latitude'],
      dtype='object')
In [7]:
#removing spaces in column names of table2
data2.columns=data2.columns.str.strip()
data2.columns
Out[7]:
Index(['Region', 'Date', 'Frequency', 'Estimated Unemployment Rate (%)',
       'Estimated Employed', 'Estimated Labour Participation Rate (%)',
       'Region.1', 'longitude', 'latitude'],
      dtype='object')
In [8]:
#checking for the region names
data2["Region"].unique()
Out[8]:
array(['Andhra Pradesh', 'Assam', 'Bihar', 'Chhattisgarh', 'Delhi', 'Goa',
       'Gujarat', 'Haryana', 'Himachal Pradesh', 'Jammu & Kashmir',
       'Jharkhand', 'Karnataka', 'Kerala', 'Madhya Pradesh',
       'Maharashtra', 'Meghalaya', 'Odisha', 'Puducherry', 'Punjab',
       'Rajasthan', 'Sikkim', 'Tamil Nadu', 'Telangana', 'Tripura',
       'Uttar Pradesh', 'Uttarakhand', 'West Bengal'], dtype=object)
In [9]:
data2["Frequency"].unique()
Out[9]:
array([' M'], dtype=object)
In [10]:
data2["Frequency"]=data2["Frequency"].str.strip()
data2["Frequency"]
Out[10]:
0      M
1      M
2      M
3      M
4      M
      ..
262    M
263    M
264    M
265    M
266    M
Name: Frequency, Length: 267, dtype: object
In [11]:
data2
Out[11]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Region.1 longitude latitude
0 Andhra Pradesh 31-01-2020 M 5.48 16635535 41.02 South 15.9129 79.740
1 Andhra Pradesh 29-02-2020 M 5.83 16545652 40.90 South 15.9129 79.740
2 Andhra Pradesh 31-03-2020 M 5.79 15881197 39.18 South 15.9129 79.740
3 Andhra Pradesh 30-04-2020 M 20.51 11336911 33.10 South 15.9129 79.740
4 Andhra Pradesh 31-05-2020 M 17.43 12988845 36.46 South 15.9129 79.740
... ... ... ... ... ... ... ... ... ...
262 West Bengal 30-06-2020 M 7.29 30726310 40.39 East 22.9868 87.855
263 West Bengal 31-07-2020 M 6.83 35372506 46.17 East 22.9868 87.855
264 West Bengal 31-08-2020 M 14.87 33298644 47.48 East 22.9868 87.855
265 West Bengal 30-09-2020 M 9.35 35707239 47.73 East 22.9868 87.855
266 West Bengal 31-10-2020 M 9.98 33962549 45.63 East 22.9868 87.855

267 rows × 9 columns

In [12]:
data2["Date"].unique()
Out[12]:
array([' 31-01-2020', ' 29-02-2020', ' 31-03-2020', ' 30-04-2020',
       ' 31-05-2020', ' 30-06-2020', ' 31-07-2020', ' 31-08-2020',
       ' 30-09-2020', ' 31-10-2020'], dtype=object)
In [13]:
data["Date"].unique()
Out[13]:
array([' 31-05-2019', ' 30-06-2019', ' 31-07-2019', ' 31-08-2019',
       ' 30-09-2019', ' 31-10-2019', ' 30-11-2019', ' 31-12-2019',
       ' 31-01-2020', ' 29-02-2020', ' 31-03-2020', ' 30-04-2020',
       ' 31-05-2020', ' 30-06-2020', nan], dtype=object)
In [14]:
#number of regions present
data["Region"].value_counts()
Out[14]:
Region
Andhra Pradesh      28
Kerala              28
West Bengal         28
Uttar Pradesh       28
Tripura             28
Telangana           28
Tamil Nadu          28
Rajasthan           28
Punjab              28
Odisha              28
Madhya Pradesh      28
Maharashtra         28
Karnataka           28
Jharkhand           28
Himachal Pradesh    28
Haryana             28
Gujarat             28
Delhi               28
Chhattisgarh        28
Bihar               28
Meghalaya           27
Uttarakhand         27
Assam               26
Puducherry          26
Goa                 24
Jammu & Kashmir     21
Sikkim              17
Chandigarh          12
Name: count, dtype: int64
In [15]:
#checking the most common number of Regions present(the mode)
data["Region"].value_counts().mode()
Out[15]:
0    28
Name: count, dtype: int64
In [16]:
#create a new dataset by combining necessary columns from both datasets
region=data["Region"].unique().tolist()
date=[]
frequency=[]
estimated_unemployment_rate=[]
estimated_employed=[]
estimated_labour_participation_rate=[]
area=[]
In [ ]:
In [17]:
for i in region:
    frequency.append((data.loc[data["Region"]== i, "Frequency"]))
    estimated_unemployment_rate.append((data2.loc[data2["Region"]== i, "Estimated Unemployment Rate (%)"]).sum())
    estimated_employed.append((data2.loc[data2["Region"]== i, "Estimated Employed"]).sum())
    estimated_labour_participation_rate.append((data.loc[data["Region"]== i, "Estimated Labour Participation Rate (%)"]).sum()/28)
    

    
aggregated_data = pd.DataFrame(list(zip(region, frequency, estimated_unemployment_rate, estimated_employed, estimated_labour_participation_rate)),
                              columns=["Region", "Frequency", "Estimated Unemployment Rate (%)", "Estimated Employed",
                                      "Estimated Labour Participation Rate (%)"])
In [18]:
aggregated_data.head()
Out[18]:
Region Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%)
0 Andhra Pradesh 0 Monthly 1 Monthly 2 Monthl... 86.64 154254800 39.375714
1 Assam 14 Monthly 15 Monthly 16 Monthl... 48.56 108102755 41.663571
2 Bihar 26 Monthly 27 Monthly 28 Monthl... 194.71 236068280 38.153929
3 Chhattisgarh 40 Monthly 41 Monthly 42 Monthl... 78.19 84213492 42.810714
4 Delhi 54 Monthly 55 Monthly 56 Monthl... 184.14 46328219 38.929643
In [19]:
#sorting data according to Estimated Unemployment Rate
datas = aggregated_data.sort_values(by=["Estimated Unemployment Rate (%)"], ascending=False)
datas.head()
Out[19]:
Region Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%)
7 Haryana 94 Monthly 95 Monthly 96 Monthl... 274.77 68440590 42.737143
23 Tripura 304 Monthly 305 Monthly 306 Monthl... 250.55 13972916 61.823929
10 Jharkhand 133 Monthly 134 Monthly 135 Monthl... 195.39 87706424 41.670714
2 Bihar 26 Monthly 27 Monthly 28 Monthl... 194.71 236068280 38.153929
4 Delhi 54 Monthly 55 Monthly 56 Monthl... 184.14 46328219 38.929643
In [20]:
#Top 10 regions with the highest Estimated Unemployment Rate
datas=datas.head(10)
datas
Out[20]:
Region Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%)
7 Haryana 94 Monthly 95 Monthly 96 Monthl... 274.77 68440590 42.737143
23 Tripura 304 Monthly 305 Monthly 306 Monthl... 250.55 13972916 61.823929
10 Jharkhand 133 Monthly 134 Monthly 135 Monthl... 195.39 87706424 41.670714
2 Bihar 26 Monthly 27 Monthly 28 Monthl... 194.71 236068280 38.153929
4 Delhi 54 Monthly 55 Monthly 56 Monthl... 184.14 46328219 38.929643
17 Puducherry 231 Monthly 232 Monthly 233 Monthl... 179.42 3652629 36.207500
8 Himachal Pradesh 108 Monthly 109 Monthly 110 Monthl... 160.65 20338849 44.222143
19 Rajasthan 257 Monthly 258 Monthly 259 Monthl... 158.68 197317522 39.973214
9 Jammu & Kashmir 122 Monthly 123 Monthly 124 Monthl... 148.30 29790285 30.773214
21 Tamil Nadu 276 Monthly 277 Monthly 278 Monthl... 121.87 219878981 40.872143
In [21]:
#total estimated employed
datas['Estimated Employed'].sum()
Out[21]:
923494695
In [ ]:
In [22]:
#Estimated employment rate of countries with highest unemployment rates
figure = px.bar(datas, y='Estimated Employed', x='Region', title='Estimated employment rate of countries with highest unemployment rates')
figure.show()
In [23]:
#Regions with the highest Unemployment cases
figure = px.bar(datas, y='Estimated Unemployment Rate (%)', x='Region', title='Regions with the highest Unemployment cases')
figure.show()
In [25]:
#Estimated Labour Participation Rate of regions with highest unemploment rates
figure = px.bar(datas, y='Estimated Labour Participation Rate (%)', x='Region', title='Estimated Labour Participation Rate of regions with highest unemploment rates')
figure.show()